Overview

Brought to you by YData

Dataset statistics

Number of variables32
Number of observations3149405
Missing cells41039307
Missing cells (%)40.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory768.9 MiB
Average record size in memory256.0 B

Variable types

Numeric11
Text8
Categorical4
DateTime6
Unsupported3

Alerts

MONTO_DE_COMISIONES has constant value "0.0" Constant
INTERES_LIMPIO_FUTURO has constant value "0.0" Constant
CUSTOMER_ID is highly overall correlated with OBJETIVO_DEL_FIDEICOMISO and 1 other fieldsHigh correlation
ID_DISPERSION is highly overall correlated with ID_TRANSACTIONHigh correlation
ID_TRANSACTION is highly overall correlated with ID_DISPERSION and 2 other fieldsHigh correlation
MONTO_BRUTO is highly overall correlated with MONTO_SERIE_PRI and 1 other fieldsHigh correlation
MONTO_DISPERSION is highly overall correlated with OBJETIVO_DEL_FIDEICOMISOHigh correlation
MONTO_ORDENADO is highly overall correlated with MONTO_SERIE_PRI and 1 other fieldsHigh correlation
MONTO_SERIE_PRI is highly overall correlated with MONTO_BRUTO and 1 other fieldsHigh correlation
OBJETIVO_DEL_FIDEICOMISO is highly overall correlated with CUSTOMER_ID and 3 other fieldsHigh correlation
OID_CUSTOMERS is highly overall correlated with CUSTOMER_ID and 1 other fieldsHigh correlation
SUBTIPO_TX is highly overall correlated with MONTO_BRUTO and 1 other fieldsHigh correlation
TASA_DE_TRANSACCION is highly overall correlated with ID_TRANSACTIONHigh correlation
RFC is highly imbalanced (91.9%) Imbalance
SUBTIPO_TX is highly imbalanced (69.9%) Imbalance
RFC has 44210 (1.4%) missing values Missing
FECHA_DE_LIQUIDACION has 90735 (2.9%) missing values Missing
FECHA_DE_FIN_DE_TRANSACCION has 1337566 (42.5%) missing values Missing
TASA_DE_TRANSACCION has 1337566 (42.5%) missing values Missing
COMENTARIOS_TX has 2346822 (74.5%) missing values Missing
CONCEPTO_TX has 2347232 (74.5%) missing values Missing
OBJETIVO_DEL_FIDEICOMISO has 2983785 (94.7%) missing values Missing
SUBTIPO_TX has 2168606 (68.9%) missing values Missing
INTERES_LIMPIO_FUTURO has 2799144 (88.9%) missing values Missing
EXCEDENTE_UTILIZADO has 3149405 (100.0%) missing values Missing
MONTO_EN_DOLARES has 3149405 (100.0%) missing values Missing
TASA_DE_CAMBIO_DOLARES has 3149405 (100.0%) missing values Missing
MONTO_BRUTO has 2796429 (88.8%) missing values Missing
MONTO_ORDENADO has 3146676 (99.9%) missing values Missing
ID_DISPERSION has 2546749 (80.9%) missing values Missing
MONTO_DISPERSION has 2546749 (80.9%) missing values Missing
NOMBRE_DE_BENEFICIARIO has 2546749 (80.9%) missing values Missing
RFC_BENEFICIARIO has 2546749 (80.9%) missing values Missing
CUSTOMER_ID is highly skewed (γ1 = 42.03372401) Skewed
MONTO_SERIE_PRI is highly skewed (γ1 = 323.4741729) Skewed
MONTO_DISPERSION is highly skewed (γ1 = 40.46518368) Skewed
EXCEDENTE_UTILIZADO is an unsupported type, check if it needs cleaning or further analysis Unsupported
MONTO_EN_DOLARES is an unsupported type, check if it needs cleaning or further analysis Unsupported
TASA_DE_CAMBIO_DOLARES is an unsupported type, check if it needs cleaning or further analysis Unsupported
TASA_DE_TRANSACCION has 57852 (1.8%) zeros Zeros

Reproduction

Analysis started2025-03-12 01:11:29.262979
Analysis finished2025-03-12 01:14:02.277926
Duration2 minutes and 33.01 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

OID_CUSTOMERS
Real number (ℝ)

High correlation 

Distinct292
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4256.0073
Minimum3087
Maximum8763
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.0 MiB
2025-03-11T19:14:02.447781image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum3087
5-th percentile3091
Q13091
median4390
Q34436
95-th percentile6317
Maximum8763
Range5676
Interquartile range (IQR)1345

Descriptive statistics

Standard deviation1257.5454
Coefficient of variation (CV)0.29547539
Kurtosis2.0715203
Mean4256.0073
Median Absolute Deviation (MAD)1299
Skewness1.3648857
Sum1.3403891 × 1010
Variance1581420.5
MonotonicityNot monotonic
2025-03-11T19:14:02.672350image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3091 1084683
34.4%
4423 486724
15.5%
3092 79434
 
2.5%
4437 74907
 
2.4%
4430 56736
 
1.8%
5907 54289
 
1.7%
4394 52818
 
1.7%
5712 52228
 
1.7%
4427 52000
 
1.7%
8586 35943
 
1.1%
Other values (282) 1119643
35.6%
ValueCountFrequency (%)
3087 10
 
< 0.1%
3089 26162
 
0.8%
3090 51
 
< 0.1%
3091 1084683
34.4%
3092 79434
 
2.5%
3096 4470
 
0.1%
3097 54
 
< 0.1%
3698 25
 
< 0.1%
3699 5762
 
0.2%
3700 151
 
< 0.1%
ValueCountFrequency (%)
8763 34
< 0.1%
8745 1
 
< 0.1%
8744 1
 
< 0.1%
8742 1
 
< 0.1%
8732 1
 
< 0.1%
8724 1
 
< 0.1%
8716 1
 
< 0.1%
8709 1
 
< 0.1%
8708 1
 
< 0.1%
8689 1
 
< 0.1%

CUSTOMER_ID
Real number (ℝ)

High correlation  Skewed 

Distinct292
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77188.937
Minimum47
Maximum8047912
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.0 MiB
2025-03-11T19:14:02.918544image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum47
5-th percentile10937
Q180139
median80280
Q380481
95-th percentile80730
Maximum8047912
Range8047865
Interquartile range (IQR)342

Descriptive statistics

Standard deviation55920.694
Coefficient of variation (CV)0.72446513
Kurtosis4926.0751
Mean77188.937
Median Absolute Deviation (MAD)141
Skewness42.033724
Sum2.4309923 × 1011
Variance3.127124 × 109
MonotonicityNot monotonic
2025-03-11T19:14:03.135126image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80139 1084683
34.4%
80320 486724
15.5%
80280 79434
 
2.5%
80481 74907
 
2.4%
80449 56736
 
1.8%
80692 54289
 
1.7%
10771 52818
 
1.7%
80672 52228
 
1.7%
80405 52000
 
1.7%
80776 35943
 
1.1%
Other values (282) 1119643
35.6%
ValueCountFrequency (%)
47 148
 
< 0.1%
237 1217
 
< 0.1%
465 2984
0.1%
515 2883
0.1%
553 25
 
< 0.1%
567 5762
0.2%
586 151
 
< 0.1%
641 132
 
< 0.1%
660 156
 
< 0.1%
1592 6972
0.2%
ValueCountFrequency (%)
8047912 6
< 0.1%
8047911 14
< 0.1%
8022274 1
 
< 0.1%
8022273 1
 
< 0.1%
8022271 1
 
< 0.1%
8022270 1
 
< 0.1%
8022265 1
 
< 0.1%
8022263 1
 
< 0.1%
8022260 1
 
< 0.1%
8022259 1
 
< 0.1%
Distinct292
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size24.0 MiB
2025-03-11T19:14:03.671407image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length106
Median length94
Mean length45.882149
Min length3

Characters and Unicode

Total characters144501469
Distinct characters81
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)< 0.1%

Sample

1st rowFideicomiso de Contragarantia para el Financiamiento Empresarial
2nd rowFideicomiso de Contragarantia para el Financiamiento Empresarial
3rd rowFideicomiso de Contragarantia para el Financiamiento Empresarial
4th rowFideicomiso de Contragarantia para el Financiamiento Empresarial
5th rowFideicomiso de Contragarantia para el Financiamiento Empresarial
ValueCountFrequency (%)
de 2436749
 
13.1%
fideicomiso 1349643
 
7.3%
para 1289561
 
7.0%
el 1172337
 
6.3%
financiamiento 1132607
 
6.1%
empresarial 1109839
 
6.0%
contragarantia 1084683
 
5.9%
fondo 802494
 
4.3%
pensiones 623950
 
3.4%
del 608667
 
3.3%
Other values (650) 6930023
37.4%
2025-03-11T19:14:04.347250image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
15457441
 
10.7%
a 13539073
 
9.4%
i 10591336
 
7.3%
e 7911714
 
5.5%
r 6909386
 
4.8%
n 6902453
 
4.8%
o 6900014
 
4.8%
E 6270125
 
4.3%
A 4223876
 
2.9%
t 4126232
 
2.9%
Other values (71) 61669819
42.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 144501469
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
15457441
 
10.7%
a 13539073
 
9.4%
i 10591336
 
7.3%
e 7911714
 
5.5%
r 6909386
 
4.8%
n 6902453
 
4.8%
o 6900014
 
4.8%
E 6270125
 
4.3%
A 4223876
 
2.9%
t 4126232
 
2.9%
Other values (71) 61669819
42.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 144501469
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
15457441
 
10.7%
a 13539073
 
9.4%
i 10591336
 
7.3%
e 7911714
 
5.5%
r 6909386
 
4.8%
n 6902453
 
4.8%
o 6900014
 
4.8%
E 6270125
 
4.3%
A 4223876
 
2.9%
t 4126232
 
2.9%
Other values (71) 61669819
42.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 144501469
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
15457441
 
10.7%
a 13539073
 
9.4%
i 10591336
 
7.3%
e 7911714
 
5.5%
r 6909386
 
4.8%
n 6902453
 
4.8%
o 6900014
 
4.8%
E 6270125
 
4.3%
A 4223876
 
2.9%
t 4126232
 
2.9%
Other values (71) 61669819
42.7%
Distinct292
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size24.0 MiB
2025-03-11T19:14:04.693186image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length106
Median length94
Mean length45.890214
Min length3

Characters and Unicode

Total characters144526869
Distinct characters81
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)< 0.1%

Sample

1st rowFideicomiso de Contragarantia para el Financiamiento Empresarial
2nd rowFideicomiso de Contragarantia para el Financiamiento Empresarial
3rd rowFideicomiso de Contragarantia para el Financiamiento Empresarial
4th rowFideicomiso de Contragarantia para el Financiamiento Empresarial
5th rowFideicomiso de Contragarantia para el Financiamiento Empresarial
ValueCountFrequency (%)
de 2436749
 
13.1%
fideicomiso 1349643
 
7.3%
para 1289561
 
7.0%
el 1172337
 
6.3%
financiamiento 1132607
 
6.1%
empresarial 1109839
 
6.0%
contragarantia 1084683
 
5.8%
fondo 802494
 
4.3%
pensiones 623950
 
3.4%
del 608667
 
3.3%
Other values (650) 6933198
37.4%
2025-03-11T19:14:05.256263image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
15460616
 
10.7%
a 13545423
 
9.4%
i 10594511
 
7.3%
e 7911714
 
5.5%
r 6909386
 
4.8%
n 6902453
 
4.8%
o 6900014
 
4.8%
E 6270125
 
4.3%
A 4223876
 
2.9%
t 4126232
 
2.9%
Other values (71) 61682519
42.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 144526869
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
15460616
 
10.7%
a 13545423
 
9.4%
i 10594511
 
7.3%
e 7911714
 
5.5%
r 6909386
 
4.8%
n 6902453
 
4.8%
o 6900014
 
4.8%
E 6270125
 
4.3%
A 4223876
 
2.9%
t 4126232
 
2.9%
Other values (71) 61682519
42.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 144526869
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
15460616
 
10.7%
a 13545423
 
9.4%
i 10594511
 
7.3%
e 7911714
 
5.5%
r 6909386
 
4.8%
n 6902453
 
4.8%
o 6900014
 
4.8%
E 6270125
 
4.3%
A 4223876
 
2.9%
t 4126232
 
2.9%
Other values (71) 61682519
42.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 144526869
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
15460616
 
10.7%
a 13545423
 
9.4%
i 10594511
 
7.3%
e 7911714
 
5.5%
r 6909386
 
4.8%
n 6902453
 
4.8%
o 6900014
 
4.8%
E 6270125
 
4.3%
A 4223876
 
2.9%
t 4126232
 
2.9%
Other values (71) 61682519
42.7%

RFC
Categorical

Imbalance  Missing 

Distinct10
Distinct (%)< 0.1%
Missing44210
Missing (%)1.4%
Memory size24.0 MiB
XXXXXXXXXXXXX
3013538 
XXXXXXXXXX
 
35943
INF7205011ZA
 
13859
XXXXXXXXXXX
 
12954
FPI220708J49
 
8536
Other values (5)
 
20365

Length

Max length13
Median length13
Mean length12.943161
Min length10

Characters and Unicode

Total characters40191038
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowXXXXXXXXXXXXX
2nd rowXXXXXXXXXXXXX
3rd rowXXXXXXXXXXXXX
4th rowXXXXXXXXXXXXX
5th rowXXXXXXXXXXXXX

Common Values

ValueCountFrequency (%)
XXXXXXXXXXXXX 3013538
95.7%
XXXXXXXXXX 35943
 
1.1%
INF7205011ZA 13859
 
0.4%
XXXXXXXXXXX 12954
 
0.4%
FPI220708J49 8536
 
0.3%
NFF9204099T4 6345
 
0.2%
XXX000000XXX 5303
 
0.2%
XXXXXXXXXXXX 4217
 
0.1%
AEF8608147R6 3487
 
0.1%
FGR1812158Z1 1013
 
< 0.1%
(Missing) 44210
 
1.4%

Length

2025-03-11T19:14:05.619085image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-11T19:14:05.791043image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
xxxxxxxxxxxxx 3013538
97.0%
xxxxxxxxxx 35943
 
1.2%
inf7205011za 13859
 
0.4%
xxxxxxxxxxx 12954
 
0.4%
fpi220708j49 8536
 
0.3%
nff9204099t4 6345
 
0.2%
xxx000000xxx 5303
 
0.2%
xxxxxxxxxxxx 4217
 
0.1%
aef8608147r6 3487
 
0.1%
fgr1812158z1 1013
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
X 39760340
98.9%
0 92785
 
0.2%
F 39585
 
0.1%
2 38289
 
0.1%
1 35257
 
0.1%
9 27571
 
0.1%
7 25882
 
0.1%
4 24713
 
0.1%
I 22395
 
0.1%
N 20204
 
0.1%
Other values (11) 104017
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 40191038
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
X 39760340
98.9%
0 92785
 
0.2%
F 39585
 
0.1%
2 38289
 
0.1%
1 35257
 
0.1%
9 27571
 
0.1%
7 25882
 
0.1%
4 24713
 
0.1%
I 22395
 
0.1%
N 20204
 
0.1%
Other values (11) 104017
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 40191038
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
X 39760340
98.9%
0 92785
 
0.2%
F 39585
 
0.1%
2 38289
 
0.1%
1 35257
 
0.1%
9 27571
 
0.1%
7 25882
 
0.1%
4 24713
 
0.1%
I 22395
 
0.1%
N 20204
 
0.1%
Other values (11) 104017
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 40191038
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
X 39760340
98.9%
0 92785
 
0.2%
F 39585
 
0.1%
2 38289
 
0.1%
1 35257
 
0.1%
9 27571
 
0.1%
7 25882
 
0.1%
4 24713
 
0.1%
I 22395
 
0.1%
N 20204
 
0.1%
Other values (11) 104017
 
0.3%
Distinct152
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size24.0 MiB
2025-03-11T19:14:06.242148image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length127
Median length123
Mean length83.610548
Min length12

Characters and Unicode

Total characters263323478
Distinct characters68
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1020|||DISTRITO FEDERAL|ALVARO OBREGON |GUADALUPE INN |INSURGENTES SUR |# 1971 TORRE IV PISO12 PLAZA INN|||||
2nd row1020|||DISTRITO FEDERAL|ALVARO OBREGON |GUADALUPE INN |INSURGENTES SUR |# 1971 TORRE IV PISO12 PLAZA INN|||||
3rd row1020|||DISTRITO FEDERAL|ALVARO OBREGON |GUADALUPE INN |INSURGENTES SUR |# 1971 TORRE IV PISO12 PLAZA INN|||||
4th row1020|||DISTRITO FEDERAL|ALVARO OBREGON |GUADALUPE INN |INSURGENTES SUR |# 1971 TORRE IV PISO12 PLAZA INN|||||
5th row1020|||DISTRITO FEDERAL|ALVARO OBREGON |GUADALUPE INN |INSURGENTES SUR |# 1971 TORRE IV PISO12 PLAZA INN|||||
ValueCountFrequency (%)
4736654
 
13.9%
inn 2311978
 
6.8%
sur 1975054
 
5.8%
federal|alvaro 1879735
 
5.5%
obregon 1814523
 
5.3%
1971 1775757
 
5.2%
guadalupe 1737978
 
5.1%
torre 1666553
 
4.9%
insurgentes 1331214
 
3.9%
1020|||distrito 1206613
 
3.6%
Other values (599) 13521795
39.8%
2025-03-11T19:14:06.788144image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
| 37792860
14.4%
31174186
 
11.8%
R 17752381
 
6.7%
E 16980198
 
6.4%
I 15726977
 
6.0%
A 15500189
 
5.9%
O 14692419
 
5.6%
N 12716788
 
4.8%
S 11550985
 
4.4%
T 9289874
 
3.5%
Other values (58) 80146621
30.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 263323478
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
| 37792860
14.4%
31174186
 
11.8%
R 17752381
 
6.7%
E 16980198
 
6.4%
I 15726977
 
6.0%
A 15500189
 
5.9%
O 14692419
 
5.6%
N 12716788
 
4.8%
S 11550985
 
4.4%
T 9289874
 
3.5%
Other values (58) 80146621
30.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 263323478
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
| 37792860
14.4%
31174186
 
11.8%
R 17752381
 
6.7%
E 16980198
 
6.4%
I 15726977
 
6.0%
A 15500189
 
5.9%
O 14692419
 
5.6%
N 12716788
 
4.8%
S 11550985
 
4.4%
T 9289874
 
3.5%
Other values (58) 80146621
30.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 263323478
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
| 37792860
14.4%
31174186
 
11.8%
R 17752381
 
6.7%
E 16980198
 
6.4%
I 15726977
 
6.0%
A 15500189
 
5.9%
O 14692419
 
5.6%
N 12716788
 
4.8%
S 11550985
 
4.4%
T 9289874
 
3.5%
Other values (58) 80146621
30.4%

OID_ACCOUNTS
Real number (ℝ)

Distinct1157
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58617.049
Minimum34141
Maximum142700
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.0 MiB
2025-03-11T19:14:06.938845image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum34141
5-th percentile34204
Q137609
median38871
Q378470
95-th percentile113463
Maximum142700
Range108559
Interquartile range (IQR)40861

Descriptive statistics

Standard deviation28938.928
Coefficient of variation (CV)0.49369473
Kurtosis-0.35977504
Mean58617.049
Median Absolute Deviation (MAD)4560
Skewness0.94224283
Sum1.8460883 × 1011
Variance8.3746156 × 108
MonotonicityNot monotonic
2025-03-11T19:14:07.117466image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38871 486724
 
15.5%
77884 54089
 
1.7%
38895 43236
 
1.4%
77886 34473
 
1.1%
136103 24784
 
0.8%
34141 18866
 
0.6%
78025 18367
 
0.6%
112769 18264
 
0.6%
95132 17612
 
0.6%
79104 17451
 
0.6%
Other values (1147) 2415539
76.7%
ValueCountFrequency (%)
34141 18866
0.6%
34142 30
 
< 0.1%
34144 292
 
< 0.1%
34146 3510
 
0.1%
34148 3350
 
0.1%
34149 3257
 
0.1%
34150 3498
 
0.1%
34151 3451
 
0.1%
34152 4500
 
0.1%
34153 3450
 
0.1%
ValueCountFrequency (%)
142700 1
 
< 0.1%
142680 1
 
< 0.1%
142660 1
 
< 0.1%
142650 1
 
< 0.1%
142649 1
 
< 0.1%
142648 1
 
< 0.1%
142586 1
 
< 0.1%
142585 1
 
< 0.1%
142460 1
 
< 0.1%
142429 34
< 0.1%

RFC2
Text

Distinct159
Distinct (%)< 0.1%
Missing4819
Missing (%)0.2%
Memory size24.0 MiB
2025-03-11T19:14:07.576402image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length14
Median length12
Mean length12.02099
Min length9

Characters and Unicode

Total characters37801036
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNFI3406305T0
2nd rowNFI3406305T0
3rd rowNFI3406305T0
4th rowNFI3406305T0
5th rowNFI3406305T0
ValueCountFrequency (%)
nfi3406305t0 1246565
39.6%
nff030630458 486724
 
15.5%
inf7205011za 100041
 
3.2%
cjf950204tl0 88116
 
2.8%
nfs030505am4 79433
 
2.5%
pac8907264g0 74905
 
2.4%
scj9502046p5 71261
 
2.3%
fda050428177 56888
 
1.8%
gec981004re5 52227
 
1.7%
nff930518f76 45689
 
1.5%
Other values (148) 842737
26.8%
2025-03-11T19:14:08.263646image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 7967115
21.1%
3 4037485
10.7%
F 3312968
 
8.8%
5 2873786
 
7.6%
4 2479383
 
6.6%
6 2252631
 
6.0%
N 2189104
 
5.8%
I 1558902
 
4.1%
T 1442261
 
3.8%
1 1360143
 
3.6%
Other values (28) 8327258
22.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 37801036
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 7967115
21.1%
3 4037485
10.7%
F 3312968
 
8.8%
5 2873786
 
7.6%
4 2479383
 
6.6%
6 2252631
 
6.0%
N 2189104
 
5.8%
I 1558902
 
4.1%
T 1442261
 
3.8%
1 1360143
 
3.6%
Other values (28) 8327258
22.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 37801036
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 7967115
21.1%
3 4037485
10.7%
F 3312968
 
8.8%
5 2873786
 
7.6%
4 2479383
 
6.6%
6 2252631
 
6.0%
N 2189104
 
5.8%
I 1558902
 
4.1%
T 1442261
 
3.8%
1 1360143
 
3.6%
Other values (28) 8327258
22.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 37801036
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 7967115
21.1%
3 4037485
10.7%
F 3312968
 
8.8%
5 2873786
 
7.6%
4 2479383
 
6.6%
6 2252631
 
6.0%
N 2189104
 
5.8%
I 1558902
 
4.1%
T 1442261
 
3.8%
1 1360143
 
3.6%
Other values (28) 8327258
22.0%

ID_TRANSACTION
Real number (ℝ)

High correlation 

Distinct2556663
Distinct (%)81.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2256200.5
Minimum273484
Maximum4014177
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.0 MiB
2025-03-11T19:14:08.448416image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum273484
5-th percentile494106.2
Q11326548
median2300809
Q33219371
95-th percentile3863952.8
Maximum4014177
Range3740693
Interquartile range (IQR)1892823

Descriptive statistics

Standard deviation1084586.7
Coefficient of variation (CV)0.48071378
Kurtosis-1.1983944
Mean2256200.5
Median Absolute Deviation (MAD)945002
Skewness-0.11352217
Sum7.1056891 × 1012
Variance1.1763282 × 1012
MonotonicityNot monotonic
2025-03-11T19:14:08.642506image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3371033 5143
 
0.2%
3371049 4632
 
0.1%
379431 2296
 
0.1%
381496 2237
 
0.1%
415634 2185
 
0.1%
353315 1997
 
0.1%
375326 1747
 
0.1%
399401 1193
 
< 0.1%
394432 1175
 
< 0.1%
439515 1098
 
< 0.1%
Other values (2556653) 3125702
99.2%
ValueCountFrequency (%)
273484 1
< 0.1%
273486 1
< 0.1%
273487 1
< 0.1%
273490 1
< 0.1%
273512 1
< 0.1%
273513 1
< 0.1%
273514 1
< 0.1%
273515 1
< 0.1%
273516 1
< 0.1%
273517 1
< 0.1%
ValueCountFrequency (%)
4014177 1
< 0.1%
4014176 1
< 0.1%
4014175 1
< 0.1%
4014174 1
< 0.1%
4014173 1
< 0.1%
4014172 1
< 0.1%
4014170 1
< 0.1%
4014169 1
< 0.1%
4014168 1
< 0.1%
4014167 1
< 0.1%
Distinct858251
Distinct (%)27.3%
Missing0
Missing (%)0.0%
Memory size24.0 MiB
Minimum2012-02-01 09:04:24
Maximum2025-02-19 18:18:40
Invalid dates0
Invalid dates (%)0.0%
2025-03-11T19:14:08.874592image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:14:09.041046image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct3297
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size24.0 MiB
Minimum2012-01-01 00:00:00
Maximum2025-02-25 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-03-11T19:14:09.202949image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:14:09.382866image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

FECHA_DE_LIQUIDACION
Date

Missing 

Distinct3291
Distinct (%)0.1%
Missing90735
Missing (%)2.9%
Memory size24.0 MiB
Minimum2012-01-01 00:00:00
Maximum2025-02-19 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-03-11T19:14:09.534554image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:14:09.692738image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct3298
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size24.0 MiB
Minimum2012-01-01 00:00:00
Maximum2025-02-26 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-03-11T19:14:09.885765image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:14:10.086643image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct3297
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size24.0 MiB
Minimum2012-01-01 00:00:00
Maximum2025-02-26 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-03-11T19:14:10.367289image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:14:10.515591image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct3289
Distinct (%)0.2%
Missing1337566
Missing (%)42.5%
Memory size24.0 MiB
Minimum2012-02-01 00:00:00
Maximum2025-03-13 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-03-11T19:14:10.696047image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:14:10.869507image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

TASA_DE_TRANSACCION
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct8177
Distinct (%)0.5%
Missing1337566
Missing (%)42.5%
Infinite0
Infinite (%)0.0%
Mean5.9382921
Minimum0
Maximum13.155491
Zeros57852
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size24.0 MiB
2025-03-11T19:14:11.061247image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.42
Q13.71
median5.58
Q37.93
95-th percentile11.16
Maximum13.155491
Range13.155491
Interquartile range (IQR)4.22

Descriptive statistics

Standard deviation2.9756611
Coefficient of variation (CV)0.50109712
Kurtosis-0.74786996
Mean5.9382921
Median Absolute Deviation (MAD)2.11
Skewness0.16770335
Sum10759229
Variance8.8545587
MonotonicityNot monotonic
2025-03-11T19:14:11.317523image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 57852
 
1.8%
4.45 35708
 
1.1%
11.16 34533
 
1.1%
3.96 34361
 
1.1%
2.93 31363
 
1.0%
2.92 29487
 
0.9%
2.94 27540
 
0.9%
4.44 22970
 
0.7%
4.43 20836
 
0.7%
11.18 19786
 
0.6%
Other values (8167) 1497403
47.5%
(Missing) 1337566
42.5%
ValueCountFrequency (%)
0 57852
1.8%
1 × 10-5103
 
< 0.1%
0.0005 71
 
< 0.1%
0.001 1
 
< 0.1%
0.005 1
 
< 0.1%
0.00694675 1
 
< 0.1%
0.00695692 1
 
< 0.1%
0.00695695 1
 
< 0.1%
0.00695774 1
 
< 0.1%
0.00695778 1
 
< 0.1%
ValueCountFrequency (%)
13.15549059 1
< 0.1%
12.8113879 1
< 0.1%
12.79090425 1
< 0.1%
12.67605634 1
< 0.1%
12.60283564 1
< 0.1%
12.45531081 1
< 0.1%
12.45028532 1
< 0.1%
12.40844464 1
< 0.1%
12.40416918 1
< 0.1%
12.35521236 1
< 0.1%

COMENTARIOS_TX
Text

Missing 

Distinct189795
Distinct (%)23.6%
Missing2346822
Missing (%)74.5%
Memory size24.0 MiB
2025-03-11T19:14:11.899813image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length500
Median length470
Mean length155.17816
Min length1

Characters and Unicode

Total characters124543354
Distinct characters132
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique172981 ?
Unique (%)21.6%

Sample

1st rowREGISTRAR EN 1317-2-2 ENTE 357 NAFIN/FIDE ELECTRODOMESTICOS POR VENCIMIENTOS NO PAGADOS DEL 1/02/2012 DE TERCEROS NAFIN BANCA PRIMER PISO AUTORIZACION COMITE DEL 2-JUL-08
2nd rowsaldo inicial prueba piloto
3rd rowPAGO DE GARANTIAS / AUT SES COM 020708 PAGO DE CONTRAGARANTIA AL FISO 11480 X GTIA PAG 1317-2-2-148 BANORTE FH 070212 CTO 1064277 DE BENEFICIARIO NINGUNO
4th rowPAGO DE GARANTIAS / RETIRO DE ACREEDORES DIVERSOS DE SANTANDER 2106-02-90-149 CTO 1064080 DE BENEFICIARIO NINGUNA
5th rowENTREGA DE INTERESER A TERCEROS / AUT SESION COMITE DEL 02-JUL-08 PAGO NAFIN POR SUBSIDIO DE TASA DEL PROGRAMA EMERGENTE TABASCO DE FET ABONO A CTA 2351-3 DE TERCERO NACIONAL FINANCIERA, S.N.C. - GARANTIAS
ValueCountFrequency (%)
de 1485881
 
8.6%
por 663893
 
3.8%
pago 657225
 
3.8%
gasto 465986
 
2.7%
servicios 455289
 
2.6%
del 452120
 
2.6%
médico 412572
 
2.4%
solicitudes 402516
 
2.3%
regionales 401941
 
2.3%
transf 396924
 
2.3%
Other values (173170) 11511558
66.5%
2025-03-11T19:14:12.600882image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
16726467
 
13.4%
o 8072680
 
6.5%
e 6553557
 
5.3%
a 5899582
 
4.7%
i 5769202
 
4.6%
s 5748052
 
4.6%
r 4509452
 
3.6%
n 3700448
 
3.0%
d 3661617
 
2.9%
E 3434289
 
2.8%
Other values (122) 60468008
48.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 124543354
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
16726467
 
13.4%
o 8072680
 
6.5%
e 6553557
 
5.3%
a 5899582
 
4.7%
i 5769202
 
4.6%
s 5748052
 
4.6%
r 4509452
 
3.6%
n 3700448
 
3.0%
d 3661617
 
2.9%
E 3434289
 
2.8%
Other values (122) 60468008
48.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 124543354
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
16726467
 
13.4%
o 8072680
 
6.5%
e 6553557
 
5.3%
a 5899582
 
4.7%
i 5769202
 
4.6%
s 5748052
 
4.6%
r 4509452
 
3.6%
n 3700448
 
3.0%
d 3661617
 
2.9%
E 3434289
 
2.8%
Other values (122) 60468008
48.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 124543354
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
16726467
 
13.4%
o 8072680
 
6.5%
e 6553557
 
5.3%
a 5899582
 
4.7%
i 5769202
 
4.6%
s 5748052
 
4.6%
r 4509452
 
3.6%
n 3700448
 
3.0%
d 3661617
 
2.9%
E 3434289
 
2.8%
Other values (122) 60468008
48.6%

CONCEPTO_TX
Text

Missing 

Distinct109
Distinct (%)< 0.1%
Missing2347232
Missing (%)74.5%
Memory size24.0 MiB
2025-03-11T19:14:13.000585image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length63
Median length19
Mean length22.794999
Min length6

Characters and Unicode

Total characters18285533
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st row235123
2nd rowAPORTACION A PATRIMONIO 2351
3rd rowAPORTACION A PATRIMONIO 2351
4th rowAPORTACION A PATRIMONIO 2351
5th rowPAGO DE GARANTIAS SIN COMISION E IVA
ValueCountFrequency (%)
entrega 480918
19.8%
patrimonial 457221
18.8%
de 270070
11.1%
pago 209347
8.6%
acreedores 152777
 
6.3%
diversos 152666
 
6.3%
a 52182
 
2.1%
e 50195
 
2.1%
patrimonio 47465
 
2.0%
iva 47190
 
1.9%
Other values (164) 508597
20.9%
2025-03-11T19:14:13.503267image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 2226260
12.2%
E 2187780
12.0%
R 1710180
9.4%
1668085
9.1%
I 1577334
8.6%
O 1486949
8.1%
N 1245854
 
6.8%
T 1199766
 
6.6%
P 892494
 
4.9%
S 817324
 
4.5%
Other values (26) 3273507
17.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18285533
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 2226260
12.2%
E 2187780
12.0%
R 1710180
9.4%
1668085
9.1%
I 1577334
8.6%
O 1486949
8.1%
N 1245854
 
6.8%
T 1199766
 
6.6%
P 892494
 
4.9%
S 817324
 
4.5%
Other values (26) 3273507
17.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18285533
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 2226260
12.2%
E 2187780
12.0%
R 1710180
9.4%
1668085
9.1%
I 1577334
8.6%
O 1486949
8.1%
N 1245854
 
6.8%
T 1199766
 
6.6%
P 892494
 
4.9%
S 817324
 
4.5%
Other values (26) 3273507
17.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18285533
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 2226260
12.2%
E 2187780
12.0%
R 1710180
9.4%
1668085
9.1%
I 1577334
8.6%
O 1486949
8.1%
N 1245854
 
6.8%
T 1199766
 
6.6%
P 892494
 
4.9%
S 817324
 
4.5%
Other values (26) 3273507
17.9%

OBJETIVO_DEL_FIDEICOMISO
Real number (ℝ)

High correlation  Missing 

Distinct430
Distinct (%)0.3%
Missing2983785
Missing (%)94.7%
Infinite0
Infinite (%)0.0%
Mean4067.9786
Minimum1613
Maximum6733
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.0 MiB
2025-03-11T19:14:13.672820image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1613
5-th percentile2927
Q12928
median3384
Q35727
95-th percentile6148
Maximum6733
Range5120
Interquartile range (IQR)2799

Descriptive statistics

Standard deviation1210.9652
Coefficient of variation (CV)0.2976823
Kurtosis-1.08985
Mean4067.9786
Median Absolute Deviation (MAD)457
Skewness0.592351
Sum6.7373861 × 108
Variance1466436.8
MonotonicityNot monotonic
2025-03-11T19:14:13.875546image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2928 32182
 
1.0%
5727 31204
 
1.0%
3373 22956
 
0.7%
3384 13990
 
0.4%
6148 8934
 
0.3%
4179 8555
 
0.3%
2927 5593
 
0.2%
4048 3669
 
0.1%
4397 3056
 
0.1%
3368 2884
 
0.1%
Other values (420) 32597
 
1.0%
(Missing) 2983785
94.7%
ValueCountFrequency (%)
1613 312
< 0.1%
1616 1
 
< 0.1%
1617 258
< 0.1%
1618 4
 
< 0.1%
1739 1
 
< 0.1%
1853 73
 
< 0.1%
1854 1
 
< 0.1%
1872 27
 
< 0.1%
1876 5
 
< 0.1%
1877 105
 
< 0.1%
ValueCountFrequency (%)
6733 1
 
< 0.1%
6732 3
< 0.1%
6731 2
< 0.1%
6727 1
 
< 0.1%
6722 1
 
< 0.1%
6721 2
< 0.1%
6717 1
 
< 0.1%
6716 1
 
< 0.1%
6711 1
 
< 0.1%
6710 1
 
< 0.1%

SUBTIPO_TX
Categorical

High correlation  Imbalance  Missing 

Distinct6
Distinct (%)< 0.1%
Missing2168606
Missing (%)68.9%
Memory size24.0 MiB
CORTO PLAZO
803541 
Conciliación SIFC - TAS
162199 
Pago de honorarios con cargo al patrimonio
 
8051
Pago de honorarios con depósito del cliente
 
4819
LARGO PLAZO
 
2172

Length

Max length43
Median length11
Mean length13.396446
Min length11

Characters and Unicode

Total characters13139221
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCORTO PLAZO
2nd rowCORTO PLAZO
3rd rowCORTO PLAZO
4th rowCORTO PLAZO
5th rowCORTO PLAZO

Common Values

ValueCountFrequency (%)
CORTO PLAZO 803541
 
25.5%
Conciliación SIFC - TAS 162199
 
5.2%
Pago de honorarios con cargo al patrimonio 8051
 
0.3%
Pago de honorarios con depósito del cliente 4819
 
0.2%
LARGO PLAZO 2172
 
0.1%
Inicialización de Acuerdos 17
 
< 0.1%
(Missing) 2168606
68.9%

Length

2025-03-11T19:14:14.050141image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-11T19:14:14.201634image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
plazo 805713
34.3%
corto 803541
34.2%
conciliación 162199
 
6.9%
sifc 162199
 
6.9%
162199
 
6.9%
tas 162199
 
6.9%
de 12887
 
0.5%
honorarios 12870
 
0.5%
con 12870
 
0.5%
pago 12870
 
0.5%
Other values (9) 40816
 
1.7%

Most occurring characters

ValueCountFrequency (%)
O 2414967
18.4%
1369564
10.4%
C 1127939
8.6%
A 970101
 
7.4%
T 965740
 
7.4%
P 818583
 
6.2%
L 807885
 
6.1%
R 805713
 
6.1%
Z 805713
 
6.1%
i 525275
 
4.0%
Other values (22) 2527741
19.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13139221
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O 2414967
18.4%
1369564
10.4%
C 1127939
8.6%
A 970101
 
7.4%
T 965740
 
7.4%
P 818583
 
6.2%
L 807885
 
6.1%
R 805713
 
6.1%
Z 805713
 
6.1%
i 525275
 
4.0%
Other values (22) 2527741
19.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13139221
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O 2414967
18.4%
1369564
10.4%
C 1127939
8.6%
A 970101
 
7.4%
T 965740
 
7.4%
P 818583
 
6.2%
L 807885
 
6.1%
R 805713
 
6.1%
Z 805713
 
6.1%
i 525275
 
4.0%
Other values (22) 2527741
19.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13139221
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O 2414967
18.4%
1369564
10.4%
C 1127939
8.6%
A 970101
 
7.4%
T 965740
 
7.4%
P 818583
 
6.2%
L 807885
 
6.1%
R 805713
 
6.1%
Z 805713
 
6.1%
i 525275
 
4.0%
Other values (22) 2527741
19.2%

MONTO_DE_COMISIONES
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing253
Missing (%)< 0.1%
Memory size24.0 MiB
0.0
3149152 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters9447456
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 3149152
> 99.9%
(Missing) 253
 
< 0.1%

Length

2025-03-11T19:14:14.399438image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-11T19:14:14.536459image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 3149152
100.0%

Most occurring characters

ValueCountFrequency (%)
0 6298304
66.7%
. 3149152
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9447456
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 6298304
66.7%
. 3149152
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9447456
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 6298304
66.7%
. 3149152
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9447456
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 6298304
66.7%
. 3149152
33.3%

INTERES_LIMPIO_FUTURO
Categorical

Constant  Missing 

Distinct1
Distinct (%)< 0.1%
Missing2799144
Missing (%)88.9%
Memory size24.0 MiB
0.0
350261 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1050783
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 350261
 
11.1%
(Missing) 2799144
88.9%

Length

2025-03-11T19:14:14.661563image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-11T19:14:14.776634image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 350261
100.0%

Most occurring characters

ValueCountFrequency (%)
0 700522
66.7%
. 350261
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1050783
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 700522
66.7%
. 350261
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1050783
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 700522
66.7%
. 350261
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1050783
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 700522
66.7%
. 350261
33.3%

EXCEDENTE_UTILIZADO
Unsupported

Missing  Rejected  Unsupported 

Missing3149405
Missing (%)100.0%
Memory size24.0 MiB

MONTO_EN_DOLARES
Unsupported

Missing  Rejected  Unsupported 

Missing3149405
Missing (%)100.0%
Memory size24.0 MiB

TASA_DE_CAMBIO_DOLARES
Unsupported

Missing  Rejected  Unsupported 

Missing3149405
Missing (%)100.0%
Memory size24.0 MiB

MONTO_SERIE_PRI
Real number (ℝ)

High correlation  Skewed 

Distinct390508
Distinct (%)12.4%
Missing253
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2480874.4
Minimum0
Maximum6.0876246 × 1010
Zeros4591
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size24.0 MiB
2025-03-11T19:14:14.892052image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.05
Q11
median1
Q3346246.14
95-th percentile8854747.8
Maximum6.0876246 × 1010
Range6.0876246 × 1010
Interquartile range (IQR)346245.14

Descriptive statistics

Standard deviation88743588
Coefficient of variation (CV)35.771093
Kurtosis163140.03
Mean2480874.4
Median Absolute Deviation (MAD)0
Skewness323.47417
Sum7.8126505 × 1012
Variance7.8754245 × 1015
MonotonicityNot monotonic
2025-03-11T19:14:15.073419image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1812407
57.5%
0.01 82995
 
2.6%
0.02 13121
 
0.4%
500000 6923
 
0.2%
2500000 5741
 
0.2%
0.03 5559
 
0.2%
65010350.19 5143
 
0.2%
0.04 4900
 
0.2%
3900586.55 4632
 
0.1%
0 4591
 
0.1%
Other values (390498) 1203140
38.2%
ValueCountFrequency (%)
0 4591
0.1%
1 × 10-867
 
< 0.1%
2 × 10-8105
 
< 0.1%
3 × 10-887
 
< 0.1%
4 × 10-8121
 
< 0.1%
5 × 10-860
 
< 0.1%
6 × 10-826
 
< 0.1%
7 × 10-81
 
< 0.1%
8 × 10-810
 
< 0.1%
1 × 10-722
 
< 0.1%
ValueCountFrequency (%)
6.087624637 × 10101
< 0.1%
5.685641939 × 10101
< 0.1%
3.724352732 × 10102
< 0.1%
3 × 10101
< 0.1%
2.93310911 × 10101
< 0.1%
2.6411527 × 10101
< 0.1%
2.467502145 × 10101
< 0.1%
2.4 × 10101
< 0.1%
1.794 × 10101
< 0.1%
1.759005459 × 10101
< 0.1%

MONTO_BRUTO
Real number (ℝ)

High correlation  Missing 

Distinct347333
Distinct (%)98.4%
Missing2796429
Missing (%)88.8%
Infinite0
Infinite (%)0.0%
Mean2.2250991 × 108
Minimum0
Maximum1.5306514 × 1010
Zeros58
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size24.0 MiB
2025-03-11T19:14:15.246353image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11950.24
Q12231941.8
median19284083
Q31.7142202 × 108
95-th percentile1.055554 × 109
Maximum1.5306514 × 1010
Range1.5306514 × 1010
Interquartile range (IQR)1.6919007 × 108

Descriptive statistics

Standard deviation5.8542822 × 108
Coefficient of variation (CV)2.6310208
Kurtosis50.313771
Mean2.2250991 × 108
Median Absolute Deviation (MAD)19234044
Skewness5.9397455
Sum7.854066 × 1013
Variance3.427262 × 1017
MonotonicityNot monotonic
2025-03-11T19:14:15.558474image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 58
 
< 0.1%
99.46 34
 
< 0.1%
99.52 31
 
< 0.1%
99.88 27
 
< 0.1%
99.98 25
 
< 0.1%
100.26 23
 
< 0.1%
99.96 23
 
< 0.1%
99.99 23
 
< 0.1%
100.13 23
 
< 0.1%
100.09 22
 
< 0.1%
Other values (347323) 352687
 
11.2%
(Missing) 2796429
88.8%
ValueCountFrequency (%)
0 58
< 0.1%
19.54 1
 
< 0.1%
48.19 1
 
< 0.1%
68.15 1
 
< 0.1%
76.08 1
 
< 0.1%
78.68 1
 
< 0.1%
79.68 1
 
< 0.1%
81.47 1
 
< 0.1%
81.98 1
 
< 0.1%
83.06 1
 
< 0.1%
ValueCountFrequency (%)
1.530651398 × 10101
< 0.1%
1.149335468 × 10101
< 0.1%
1.148472497 × 10101
< 0.1%
1.146398509 × 10101
< 0.1%
1.144696441 × 10101
< 0.1%
1.14263681 × 10101
< 0.1%
1.068098806 × 10101
< 0.1%
1.06455167 × 10101
< 0.1%
9899999983 1
< 0.1%
9899999952 1
< 0.1%

MONTO_ORDENADO
Real number (ℝ)

High correlation  Missing 

Distinct1875
Distinct (%)68.7%
Missing3146676
Missing (%)99.9%
Infinite0
Infinite (%)0.0%
Mean14277963
Minimum0
Maximum3.0146884 × 109
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size24.0 MiB
2025-03-11T19:14:15.764297image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile707.072
Q110560
median65564
Q3363622.91
95-th percentile9560088.2
Maximum3.0146884 × 109
Range3.0146884 × 109
Interquartile range (IQR)353062.91

Descriptive statistics

Standard deviation1.3330314 × 108
Coefficient of variation (CV)9.3362856
Kurtosis259.97368
Mean14277963
Median Absolute Deviation (MAD)64364
Skewness14.645131
Sum3.8964562 × 1010
Variance1.7769728 × 1016
MonotonicityNot monotonic
2025-03-11T19:14:15.946123image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 23
 
< 0.1%
50000 18
 
< 0.1%
3427.8 18
 
< 0.1%
3638.92 16
 
< 0.1%
100000 16
 
< 0.1%
712.71 14
 
< 0.1%
1097.15 13
 
< 0.1%
10587.5 13
 
< 0.1%
20114.43 13
 
< 0.1%
13066.43 13
 
< 0.1%
Other values (1865) 2572
 
0.1%
(Missing) 3146676
99.9%
ValueCountFrequency (%)
0 1
 
< 0.1%
0.01 1
 
< 0.1%
0.05 2
 
< 0.1%
0.07 3
 
< 0.1%
1 8
< 0.1%
1.4 1
 
< 0.1%
10 5
< 0.1%
11 1
 
< 0.1%
16 1
 
< 0.1%
17 1
 
< 0.1%
ValueCountFrequency (%)
3014688352 1
< 0.1%
3014688352 1
< 0.1%
2269060503 1
< 0.1%
1809727100 1
< 0.1%
1444601261 1
< 0.1%
1431330981 1
< 0.1%
1410378506 2
< 0.1%
1244586711 2
< 0.1%
1192070325 1
< 0.1%
983850662.7 1
< 0.1%

ID_DISPERSION
Real number (ℝ)

High correlation  Missing 

Distinct602656
Distinct (%)100.0%
Missing2546749
Missing (%)80.9%
Infinite0
Infinite (%)0.0%
Mean309215.89
Minimum1
Maximum623592
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.0 MiB
2025-03-11T19:14:16.138455image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile30133.75
Q1152012.75
median310160.5
Q3463934.25
95-th percentile593221.25
Maximum623592
Range623591
Interquartile range (IQR)311921.5

Descriptive statistics

Standard deviation179975.83
Coefficient of variation (CV)0.58203939
Kurtosis-1.197351
Mean309215.89
Median Absolute Deviation (MAD)155955
Skewness0.0080813692
Sum1.8635081 × 1011
Variance3.2391299 × 1010
MonotonicityNot monotonic
2025-03-11T19:14:16.311489image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
413455 1
 
< 0.1%
413438 1
 
< 0.1%
413439 1
 
< 0.1%
413440 1
 
< 0.1%
413441 1
 
< 0.1%
413442 1
 
< 0.1%
413443 1
 
< 0.1%
413444 1
 
< 0.1%
413445 1
 
< 0.1%
413446 1
 
< 0.1%
Other values (602646) 602646
 
19.1%
(Missing) 2546749
80.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
623592 1
< 0.1%
623591 1
< 0.1%
623590 1
< 0.1%
623589 1
< 0.1%
623588 1
< 0.1%
623587 1
< 0.1%
623586 1
< 0.1%
623585 1
< 0.1%
623584 1
< 0.1%
623583 1
< 0.1%

MONTO_DISPERSION
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct235350
Distinct (%)39.1%
Missing2546749
Missing (%)80.9%
Infinite0
Infinite (%)0.0%
Mean42206.905
Minimum0.2
Maximum40000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.0 MiB
2025-03-11T19:14:16.496230image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile413.97
Q12274.33
median5133.69
Q319061.147
95-th percentile108627.82
Maximum40000000
Range40000000
Interquartile range (IQR)16786.817

Descriptive statistics

Standard deviation284137.24
Coefficient of variation (CV)6.7320085
Kurtosis3323.9535
Mean42206.905
Median Absolute Deviation (MAD)4017.69
Skewness40.465184
Sum2.5436245 × 1010
Variance8.0733974 × 1010
MonotonicityNot monotonic
2025-03-11T19:14:16.697277image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000 25842
 
0.8%
50000 8626
 
0.3%
2317.5 6099
 
0.2%
40000 4865
 
0.2%
4765.5 4372
 
0.1%
80000 3750
 
0.1%
5989.5 3648
 
0.1%
5308.51 3433
 
0.1%
2654.25 2928
 
0.1%
7213.5 2835
 
0.1%
Other values (235340) 536258
 
17.0%
(Missing) 2546749
80.9%
ValueCountFrequency (%)
0.2 1
 
< 0.1%
0.58 46
< 0.1%
1 22
 
< 0.1%
1.16 63
< 0.1%
1.28 1
 
< 0.1%
1.37 1
 
< 0.1%
1.38 1
 
< 0.1%
1.46 2
 
< 0.1%
1.54 1
 
< 0.1%
1.74 1
 
< 0.1%
ValueCountFrequency (%)
40000000 1
< 0.1%
39013800 1
< 0.1%
32493121.27 1
< 0.1%
31230000 1
< 0.1%
30000000 2
< 0.1%
27424740 1
< 0.1%
25091250 1
< 0.1%
24305000 1
< 0.1%
23623533.97 1
< 0.1%
22495931 1
< 0.1%
Distinct37387
Distinct (%)6.2%
Missing2546749
Missing (%)80.9%
Memory size24.0 MiB
2025-03-11T19:14:17.146677image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length90
Median length85
Mean length27.111063
Min length6

Characters and Unicode

Total characters16338645
Distinct characters96
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17700 ?
Unique (%)2.9%

Sample

1st rowFARMACIAS ABC DE MEXICO SA DE
2nd rowFARMACIAS ABC DE MEXICO SA DE
3rd rowFARMACIAS ABC DE MEXICO SA DE
4th rowFARMACIAS ABC DE MEXICO SA DE
5th rowFARMACIAS ABC DE MEXICO SA DE
ValueCountFrequency (%)
de 287519
 
11.1%
sa 147745
 
5.7%
cv 140399
 
5.4%
farmacias 45823
 
1.8%
hospitales 45389
 
1.8%
operadora 44715
 
1.7%
angele 40230
 
1.6%
medica 31503
 
1.2%
maria 31443
 
1.2%
benavides 30400
 
1.2%
Other values (13759) 1744896
67.4%
2025-03-11T19:14:17.779419image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 2197274
13.4%
1991598
12.2%
E 1619366
9.9%
R 1196150
 
7.3%
O 1115568
 
6.8%
I 1017956
 
6.2%
S 975507
 
6.0%
D 760811
 
4.7%
L 736129
 
4.5%
N 721280
 
4.4%
Other values (86) 4007006
24.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16338645
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 2197274
13.4%
1991598
12.2%
E 1619366
9.9%
R 1196150
 
7.3%
O 1115568
 
6.8%
I 1017956
 
6.2%
S 975507
 
6.0%
D 760811
 
4.7%
L 736129
 
4.5%
N 721280
 
4.4%
Other values (86) 4007006
24.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16338645
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 2197274
13.4%
1991598
12.2%
E 1619366
9.9%
R 1196150
 
7.3%
O 1115568
 
6.8%
I 1017956
 
6.2%
S 975507
 
6.0%
D 760811
 
4.7%
L 736129
 
4.5%
N 721280
 
4.4%
Other values (86) 4007006
24.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16338645
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 2197274
13.4%
1991598
12.2%
E 1619366
9.9%
R 1196150
 
7.3%
O 1115568
 
6.8%
I 1017956
 
6.2%
S 975507
 
6.0%
D 760811
 
4.7%
L 736129
 
4.5%
N 721280
 
4.4%
Other values (86) 4007006
24.5%

RFC_BENEFICIARIO
Text

Missing 

Distinct35999
Distinct (%)6.0%
Missing2546749
Missing (%)80.9%
Memory size24.0 MiB
2025-03-11T19:14:18.160574image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length13
Median length13
Mean length12.56618
Min length10

Characters and Unicode

Total characters7573084
Distinct characters42
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17273 ?
Unique (%)2.9%

Sample

1st rowFAB8412112X2
2nd rowFAB8412112X2
3rd rowFAB8412112X2
4th rowFAB8412112X2
5th rowFAB8412112X2
ValueCountFrequency (%)
oha051017ke7 44101
 
7.3%
fbe9110215z3 30292
 
5.0%
sme001012ri2 23563
 
3.9%
shm9610301fa 7400
 
1.2%
inf891031lt4 6556
 
1.1%
far970429se2 5634
 
0.9%
def920101uya 4527
 
0.8%
cfa930628et2 4149
 
0.7%
fra900718daa 3968
 
0.7%
amr901112qc7 3912
 
0.6%
Other values (35994) 468909
77.8%
2025-03-11T19:14:18.716067image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 945797
 
12.5%
1 825962
 
10.9%
2 498030
 
6.6%
A 404772
 
5.3%
5 377547
 
5.0%
7 364386
 
4.8%
9 319689
 
4.2%
3 295934
 
3.9%
8 289119
 
3.8%
4 275775
 
3.6%
Other values (32) 2976073
39.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7573084
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 945797
 
12.5%
1 825962
 
10.9%
2 498030
 
6.6%
A 404772
 
5.3%
5 377547
 
5.0%
7 364386
 
4.8%
9 319689
 
4.2%
3 295934
 
3.9%
8 289119
 
3.8%
4 275775
 
3.6%
Other values (32) 2976073
39.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7573084
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 945797
 
12.5%
1 825962
 
10.9%
2 498030
 
6.6%
A 404772
 
5.3%
5 377547
 
5.0%
7 364386
 
4.8%
9 319689
 
4.2%
3 295934
 
3.9%
8 289119
 
3.8%
4 275775
 
3.6%
Other values (32) 2976073
39.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7573084
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 945797
 
12.5%
1 825962
 
10.9%
2 498030
 
6.6%
A 404772
 
5.3%
5 377547
 
5.0%
7 364386
 
4.8%
9 319689
 
4.2%
3 295934
 
3.9%
8 289119
 
3.8%
4 275775
 
3.6%
Other values (32) 2976073
39.3%

Interactions

2025-03-11T19:13:32.795221image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:09.818593image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:12.749617image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:15.686266image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:18.681425image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:21.589195image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:23.635564image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:25.347974image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:28.042703image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:29.647454image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:31.069337image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:32.963916image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:10.184025image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:13.050459image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:16.039338image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:19.019578image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:21.872879image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:23.809692image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:25.700058image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:28.194190image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:29.793049image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:31.251254image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:33.167255image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:10.593293image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:13.384483image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:16.382314image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:19.392953image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:22.124564image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:23.983548image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:26.034356image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:28.452615image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:29.925757image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:31.428546image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:33.352902image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:10.923466image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:13.819611image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:16.751451image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:19.700548image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:22.358936image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:24.119596image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:26.499275image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:28.615628image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:30.062724image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:31.601194image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:33.484458image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:11.211694image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:14.100861image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:17.094280image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:19.968611image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:22.676260image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:24.242886image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:26.833505image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:28.755365image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:30.211098image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:31.753007image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:33.638671image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:11.338411image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:14.244880image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:17.257428image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:20.107846image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:22.822966image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:24.341450image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:26.960629image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:28.847345image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:30.333052image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:31.867654image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:33.848262image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:11.655941image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:14.576404image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:17.679653image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:20.511806image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:23.068415image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:24.469972image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:27.267064image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:28.963527image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:30.444352image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:32.064142image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:33.976036image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:11.812901image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:14.746956image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:17.834114image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:20.679762image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:23.181187image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:24.589282image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:27.413587image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:29.124947image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:30.591679image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:32.195540image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:34.084705image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:11.972044image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:14.922472image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:17.987076image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:20.834054image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:23.286671image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:24.692502image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:27.544362image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:29.277993image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:30.694107image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:32.299617image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:34.217338image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:12.170343image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:15.113141image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:18.168842image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:21.024936image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:23.387753image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:24.805742image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:27.728936image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:29.402948image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:30.815148image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:32.420290image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:34.513873image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:12.332682image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:15.291941image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:18.338647image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:21.238480image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:23.504145image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:24.962248image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:27.893718image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:29.536017image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:30.925189image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-03-11T19:13:32.600667image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Correlations

2025-03-11T19:14:18.849728image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
CUSTOMER_IDID_DISPERSIONID_TRANSACTIONMONTO_BRUTOMONTO_DISPERSIONMONTO_ORDENADOMONTO_SERIE_PRIOBJETIVO_DEL_FIDEICOMISOOID_ACCOUNTSOID_CUSTOMERSRFCSUBTIPO_TXTASA_DE_TRANSACCION
CUSTOMER_ID1.0000.3890.1100.0850.0740.1420.3560.6970.4000.7750.0260.045-0.063
ID_DISPERSION0.3891.0001.000NaN0.022NaN-0.2480.2610.3930.3890.4950.097NaN
ID_TRANSACTION0.1101.0001.000-0.1990.022-0.153-0.1100.5120.3350.1040.1100.1300.656
MONTO_BRUTO0.085NaN-0.1991.000NaNNaN0.958NaN0.0560.0790.1181.000NaN
MONTO_DISPERSION0.0740.0220.022NaN1.000NaN0.3410.6130.0640.0740.0020.000NaN
MONTO_ORDENADO0.142NaN-0.153NaNNaN1.0000.878NaN-0.2070.1420.0781.000NaN
MONTO_SERIE_PRI0.356-0.248-0.1100.9580.3410.8781.0000.2740.0770.4060.0140.007-0.025
OBJETIVO_DEL_FIDEICOMISO0.6970.2610.512NaN0.613NaN0.2741.0000.2070.7030.2160.369NaN
OID_ACCOUNTS0.4000.3930.3350.0560.064-0.2070.0770.2071.0000.4050.3850.1890.169
OID_CUSTOMERS0.7750.3890.1040.0790.0740.1420.4060.7030.4051.0000.3570.205-0.134
RFC0.0260.4950.1100.1180.0020.0780.0140.2160.3850.3571.0000.0670.054
SUBTIPO_TX0.0450.0970.1301.0000.0001.0000.0070.3690.1890.2050.0671.0000.000
TASA_DE_TRANSACCION-0.063NaN0.656NaNNaNNaN-0.025NaN0.169-0.1340.0540.0001.000

Missing values

2025-03-11T19:13:35.917340image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-11T19:13:43.513362image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-03-11T19:13:58.454223image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

OID_CUSTOMERSCUSTOMER_IDLEGAL_NAMEDESCRIPTIONRFCADDRESS_DUMPOID_ACCOUNTSRFC2ID_TRANSACTIONFECHA_DE_ENTRADAFECHA_DE_ACUERDOFECHA_DE_LIQUIDACIONFECHA_DE_ACUERDO_SERIE_PRIFECHA_DE_ACUERDO_SERIE_SECFECHA_DE_FIN_DE_TRANSACCIONTASA_DE_TRANSACCIONCOMENTARIOS_TXCONCEPTO_TXOBJETIVO_DEL_FIDEICOMISOSUBTIPO_TXMONTO_DE_COMISIONESINTERES_LIMPIO_FUTUROEXCEDENTE_UTILIZADOMONTO_EN_DOLARESTASA_DE_CAMBIO_DOLARESMONTO_SERIE_PRIMONTO_BRUTOMONTO_ORDENADOID_DISPERSIONMONTO_DISPERSIONNOMBRE_DE_BENEFICIARIORFC_BENEFICIARIO
0309180139Fideicomiso de Contragarantia para el Financiamiento EmpresarialFideicomiso de Contragarantia para el Financiamiento EmpresarialXXXXXXXXXXXXX1020|||DISTRITO FEDERAL|ALVARO OBREGON |GUADALUPE INN |INSURGENTES SUR |# 1971 TORRE IV PISO12 PLAZA INN|||||34323NFI3406305T02734842012-02-01 11:31:172012-02-01 00:00:002012-02-01 00:00:002012-02-01 00:00:002012-02-01 00:00:00NaNNaNREGISTRAR EN 1317-2-2 ENTE 357 NAFIN/FIDE ELECTRODOMESTICOS POR VENCIMIENTOS NO PAGADOS DEL 1/02/2012 DE TERCEROS NAFIN BANCA PRIMER PISO AUTORIZACION COMITE DEL 2-JUL-08\r\n\r\n235123NaNCORTO PLAZO0.0NaNNaNNaNNaN9.975076e+05NaNNaNNaNNaNNaNNaN
1309180139Fideicomiso de Contragarantia para el Financiamiento EmpresarialFideicomiso de Contragarantia para el Financiamiento EmpresarialXXXXXXXXXXXXX1020|||DISTRITO FEDERAL|ALVARO OBREGON |GUADALUPE INN |INSURGENTES SUR |# 1971 TORRE IV PISO12 PLAZA INN|||||34355NFI3406305T02734862012-02-01 14:59:262012-02-01 00:00:00NaN2012-02-01 00:00:002012-02-01 00:00:00NaNNaNsaldo inicial prueba pilotoAPORTACION A PATRIMONIO 2351NaNCORTO PLAZO0.0NaNNaNNaNNaN1.788668e+05NaNNaNNaNNaNNaNNaN
2309180139Fideicomiso de Contragarantia para el Financiamiento EmpresarialFideicomiso de Contragarantia para el Financiamiento EmpresarialXXXXXXXXXXXXX1020|||DISTRITO FEDERAL|ALVARO OBREGON |GUADALUPE INN |INSURGENTES SUR |# 1971 TORRE IV PISO12 PLAZA INN|||||34323NFI3406305T02734872012-02-01 15:01:112012-02-01 00:00:002012-02-01 00:00:002012-02-01 00:00:002012-02-01 00:00:00NaNNaNNaNAPORTACION A PATRIMONIO 2351NaNCORTO PLAZO0.0NaNNaNNaNNaN2.271006e+08NaNNaNNaNNaNNaNNaN
3309180139Fideicomiso de Contragarantia para el Financiamiento EmpresarialFideicomiso de Contragarantia para el Financiamiento EmpresarialXXXXXXXXXXXXX1020|||DISTRITO FEDERAL|ALVARO OBREGON |GUADALUPE INN |INSURGENTES SUR |# 1971 TORRE IV PISO12 PLAZA INN|||||34355NFI3406305T02734902012-02-01 15:12:312012-02-01 00:00:002012-02-01 00:00:002012-02-01 00:00:002012-02-01 00:00:00NaNNaNNaNAPORTACION A PATRIMONIO 2351NaNCORTO PLAZO0.0NaNNaNNaNNaN1.788668e+05NaNNaNNaNNaNNaNNaN
4309180139Fideicomiso de Contragarantia para el Financiamiento EmpresarialFideicomiso de Contragarantia para el Financiamiento EmpresarialXXXXXXXXXXXXX1020|||DISTRITO FEDERAL|ALVARO OBREGON |GUADALUPE INN |INSURGENTES SUR |# 1971 TORRE IV PISO12 PLAZA INN|||||34376NFI3406305T02738142012-02-01 14:03:372012-02-08 00:00:002012-02-08 00:00:002012-02-08 00:00:002012-02-08 00:00:00NaNNaNPAGO DE GARANTIAS / AUT SES COM 020708 PAGO DE CONTRAGARANTIA AL FISO 11480 X GTIA PAG 1317-2-2-148 BANORTE FH 070212 CTO 1064277 DE BENEFICIARIO NINGUNOPAGO DE GARANTIAS SIN COMISION E IVANaNCORTO PLAZO0.0NaNNaNNaNNaN2.380695e+06NaNNaNNaNNaNNaNNaN
5309180139Fideicomiso de Contragarantia para el Financiamiento EmpresarialFideicomiso de Contragarantia para el Financiamiento EmpresarialXXXXXXXXXXXXX1020|||DISTRITO FEDERAL|ALVARO OBREGON |GUADALUPE INN |INSURGENTES SUR |# 1971 TORRE IV PISO12 PLAZA INN|||||34329NFI3406305T02738162012-02-01 14:08:062012-02-08 00:00:002012-02-08 00:00:002012-02-08 00:00:002012-02-08 00:00:00NaNNaNPAGO DE GARANTIAS / RETIRO DE ACREEDORES DIVERSOS DE SANTANDER 2106-02-90-149 CTO 1064080 DE BENEFICIARIO NINGUNAPAGO DE GARANTIAS SIN COMISION E IVANaNCORTO PLAZO0.0NaNNaNNaNNaN1.306274e+04NaNNaNNaNNaNNaNNaN
6309180139Fideicomiso de Contragarantia para el Financiamiento EmpresarialFideicomiso de Contragarantia para el Financiamiento EmpresarialXXXXXXXXXXXXX1020|||DISTRITO FEDERAL|ALVARO OBREGON |GUADALUPE INN |INSURGENTES SUR |# 1971 TORRE IV PISO12 PLAZA INN|||||34247NFI3406305T02738172012-02-01 14:10:362012-02-08 00:00:002012-02-08 00:00:002012-02-08 00:00:002012-02-08 00:00:00NaNNaNENTREGA DE INTERESER A TERCEROS / AUT SESION COMITE DEL 02-JUL-08 PAGO NAFIN POR SUBSIDIO DE TASA DEL PROGRAMA EMERGENTE TABASCO DE FET ABONO A CTA 2351-3 DE TERCERO NACIONAL FINANCIERA, S.N.C. - GARANTIASENTREGA DE INTERESES A TERCEROSNaNCORTO PLAZO0.0NaNNaNNaNNaN5.769100e+02NaNNaNNaNNaNNaNNaN
7309180139Fideicomiso de Contragarantia para el Financiamiento EmpresarialFideicomiso de Contragarantia para el Financiamiento EmpresarialXXXXXXXXXXXXX1020|||DISTRITO FEDERAL|ALVARO OBREGON |GUADALUPE INN |INSURGENTES SUR |# 1971 TORRE IV PISO12 PLAZA INN|||||34241NFI3406305T02738182012-02-01 14:12:562012-02-08 00:00:002012-02-08 00:00:002012-02-08 00:00:002012-02-08 00:00:00NaNNaNENTREGA DE INTERESER A TERCEROS / AUT SESION COMITE DEL 02-JUL-08 PAGO NAFIN POR SUBSIDIO DE TASA DEL PROGRAMA EMERGENTE DEAN CHETUMAL A NAFIN DE TERCERO NACIONAL FINANCIERA, S.N.C. - GARANTIASENTREGA DE INTERESES A TERCEROSNaNCORTO PLAZO0.0NaNNaNNaNNaN5.623400e+02NaNNaNNaNNaNNaNNaN
8309180139Fideicomiso de Contragarantia para el Financiamiento EmpresarialFideicomiso de Contragarantia para el Financiamiento EmpresarialXXXXXXXXXXXXX1020|||DISTRITO FEDERAL|ALVARO OBREGON |GUADALUPE INN |INSURGENTES SUR |# 1971 TORRE IV PISO12 PLAZA INN|||||34161NFI3406305T02738192012-02-01 14:14:272012-02-08 00:00:002012-02-08 00:00:002012-02-08 00:00:002012-02-08 00:00:00NaNNaNPAGO DE GARANTIAS / AUT SES COM 020708 PAGO DE CONTRAGARANTIA AL FISO 11480 X GTIA PAG 1317-2-2-199 BAJIO FH 080212 CTO 1062006 DE BENEFICIARIO NINGUNOPAGO DE GARANTIAS SIN COMISION E IVANaNCORTO PLAZO0.0NaNNaNNaNNaN3.540253e+05NaNNaNNaNNaNNaNNaN
9309180139Fideicomiso de Contragarantia para el Financiamiento EmpresarialFideicomiso de Contragarantia para el Financiamiento EmpresarialXXXXXXXXXXXXX1020|||DISTRITO FEDERAL|ALVARO OBREGON |GUADALUPE INN |INSURGENTES SUR |# 1971 TORRE IV PISO12 PLAZA INN|||||34376NFI3406305T02738202012-02-01 14:15:322012-02-08 00:00:002012-02-08 00:00:002012-02-08 00:00:002012-02-08 00:00:00NaNNaNPAGO DE GARANTIAS / AUT SES COM 020708 PAGO DE CONTRAGARANTIA AL FISO 11480 X GTIA PAG 1317-2-2-199 BAJIO FH 080212 CTO 1064277 DE BENEFICIARIO NINGUNOPAGO DE GARANTIAS SIN COMISION E IVANaNCORTO PLAZO0.0NaNNaNNaNNaN4.432477e+06NaNNaNNaNNaNNaNNaN
OID_CUSTOMERSCUSTOMER_IDLEGAL_NAMEDESCRIPTIONRFCADDRESS_DUMPOID_ACCOUNTSRFC2ID_TRANSACTIONFECHA_DE_ENTRADAFECHA_DE_ACUERDOFECHA_DE_LIQUIDACIONFECHA_DE_ACUERDO_SERIE_PRIFECHA_DE_ACUERDO_SERIE_SECFECHA_DE_FIN_DE_TRANSACCIONTASA_DE_TRANSACCIONCOMENTARIOS_TXCONCEPTO_TXOBJETIVO_DEL_FIDEICOMISOSUBTIPO_TXMONTO_DE_COMISIONESINTERES_LIMPIO_FUTUROEXCEDENTE_UTILIZADOMONTO_EN_DOLARESTASA_DE_CAMBIO_DOLARESMONTO_SERIE_PRIMONTO_BRUTOMONTO_ORDENADOID_DISPERSIONMONTO_DISPERSIONNOMBRE_DE_BENEFICIARIORFC_BENEFICIARIO
3149395447880615FIDEICOMISO DE COBRANZAFIDEICOMISO DE COBRANZAXXXXXXXXXXXXX11000| ||DISTRITO FEDERAL|MEXICO |LOMAS DE CHAPULTEPEC |AV. PASEO DE LAS PALMAS |# 405-404||| ||74306PIR090827MJ740141522025-02-19 17:47:252025-02-20 00:00:00NaN2025-02-21 00:00:002025-02-21 00:00:00NaNNaNPago a las facturas 386/44741 y 386/44935 a nombre de Regus Management de México, S.A. de C.V. con fecha 05/01/2025 y 06/02/2025 respectivamente, por concepto de Pago de renta de oficina virtual por los meses de febrero y marzo 2025 para Promotora de Infraestructura Registral II, S.A. de C.V., SOFOM., E.R., Cuenta destino a nombre de México Business Center 39, SA de CV, Filial de Regus Management de México, S.A. de C.V.PAGO DE ACREEDORES DIVERSOSNaNCORTO PLAZO0.0NaNNaNNaNNaN306.24NaNNaNNaNNaNNaNNaN
3149396442380320FONDO DE PENSIONES DEL SISTEMA BANRURALFONDO DE PENSIONES DEL SISTEMA BANRURALXXXXXXXXXXXXX1020| ||DISTRITO FEDERAL|ALVARO OBREGON |GUADALUPE INN|INSURGENTES SUR |# 1971 TORRE III PISO 6 ||| ||38871NFF03063045840141672025-02-19 17:58:162025-02-20 00:00:00NaN2025-02-21 00:00:002025-02-21 00:00:00NaNNaNPago a TALLER MONTESSORI DE MERIDA AC por concepto de servicio de arrendamiento de inmuebles del Enlace Regional Mérida, correspondiente al mes de diciembre de 2024, según factura con folio fiscal 71c3671c-21d6-41dd-8397-110c486d0c6f, contrato UR-FPSB-012-2024-911. (Subp pptal 7430). Gasto que corresponde al ejercicio 2024 y se liquida en el año 2025.ENTREGA PATRIMONIALNaNCORTO PLAZO0.0NaNNaNNaNNaN20543.60NaNNaNNaNNaNNaNNaN
3149397442380320FONDO DE PENSIONES DEL SISTEMA BANRURALFONDO DE PENSIONES DEL SISTEMA BANRURALXXXXXXXXXXXXX1020| ||DISTRITO FEDERAL|ALVARO OBREGON |GUADALUPE INN|INSURGENTES SUR |# 1971 TORRE III PISO 6 ||| ||38871NFF03063045840141692025-02-19 18:02:492025-02-20 00:00:00NaN2025-02-21 00:00:002025-02-21 00:00:00NaNNaNPago a ANTONIO OCHOA PALMA por concepto de servicio de arrendamiento de inmuebles del Enlace Regional Mazatlán localidad LOS MOCHIS, correspondiente al mes de diciembre de 2024, según factura con folio fiscal C92919F2-0FC4-5636-8A65-AA251524B0F1, contrato UR-FPSB-004-2024-905. (Subp pptal 7430). Gasto que corresponde al ejercicio 2024 y se liquida en el año 2025.ENTREGA PATRIMONIALNaNCORTO PLAZO0.0NaNNaNNaNNaN9926.10NaNNaNNaNNaNNaNNaN
3149398442380320FONDO DE PENSIONES DEL SISTEMA BANRURALFONDO DE PENSIONES DEL SISTEMA BANRURALXXXXXXXXXXXXX1020| ||DISTRITO FEDERAL|ALVARO OBREGON |GUADALUPE INN|INSURGENTES SUR |# 1971 TORRE III PISO 6 ||| ||38871NFF03063045840141702025-02-19 18:04:492025-02-20 00:00:00NaN2025-02-21 00:00:002025-02-21 00:00:00NaNNaNPago a INMUEBLES DE OPORTUNIDAD, S.A. DE C.V. por concepto de servicio de arrendamiento de inmuebles del Enlace Regional Veracruz, correspondiente al mes de diciembre de 2024, según factura con folio fiscal 5E2E1337-BD4D-4624-8872-4E48CD08C757, contrato UR-FPSB-011-2024-910. (Subp pptal 7430). Gasto que corresponde al ejercicio 2024 y se liquida en el año 2025.ENTREGA PATRIMONIALNaNCORTO PLAZO0.0NaNNaNNaNNaN20000.00NaNNaNNaNNaNNaNNaN
3149399442380320FONDO DE PENSIONES DEL SISTEMA BANRURALFONDO DE PENSIONES DEL SISTEMA BANRURALXXXXXXXXXXXXX1020| ||DISTRITO FEDERAL|ALVARO OBREGON |GUADALUPE INN|INSURGENTES SUR |# 1971 TORRE III PISO 6 ||| ||38871NFF03063045840141722025-02-19 18:07:252025-02-20 00:00:00NaN2025-02-21 00:00:002025-02-21 00:00:00NaNNaNPago a FUNDOS URBANOS MEXICANOS, S.A. DE C.V. por concepto de servicio de arrendamiento de inmuebles del Enlace Regional Torreón, correspondiente al mes de diciembre de 2024, según factura con folio fiscal 708B4F2C-F8DE-4307-A871-64EE9DF73E51, contrato UR-FPSB-002-2024-903. (Subp pptal 7430). Gasto que corresponde al ejercicio 2024 y se liquida en el año 2025.ENTREGA PATRIMONIALNaNCORTO PLAZO0.0NaNNaNNaNNaN19920.10NaNNaNNaNNaNNaNNaN
3149400442380320FONDO DE PENSIONES DEL SISTEMA BANRURALFONDO DE PENSIONES DEL SISTEMA BANRURALXXXXXXXXXXXXX1020| ||DISTRITO FEDERAL|ALVARO OBREGON |GUADALUPE INN|INSURGENTES SUR |# 1971 TORRE III PISO 6 ||| ||38871NFF03063045840141732025-02-19 18:09:282025-02-20 00:00:00NaN2025-02-21 00:00:002025-02-21 00:00:00NaNNaNPago a TURRUBIATES ORTÍZ ESMERALDA MÓNICA por concepto de servicio de arrendamiento de inmuebles del Enlace Regional Cd. Victoria, correspondiente al mes de diciembre de 2024, según factura con folio fiscal 9d156924-5da5-4fc2-9a1f-82b3d1615df9,contrato UR-FPSB-003-2024-904. (Subp pptal 7430). Gasto que corresponde al ejercicio 2024 y se liquida en el año 2025.ENTREGA PATRIMONIALNaNCORTO PLAZO0.0NaNNaNNaNNaN8103.33NaNNaNNaNNaNNaNNaN
3149401442380320FONDO DE PENSIONES DEL SISTEMA BANRURALFONDO DE PENSIONES DEL SISTEMA BANRURALXXXXXXXXXXXXX1020| ||DISTRITO FEDERAL|ALVARO OBREGON |GUADALUPE INN|INSURGENTES SUR |# 1971 TORRE III PISO 6 ||| ||38871NFF03063045840141742025-02-19 18:11:402025-02-20 00:00:00NaN2025-02-21 00:00:002025-02-21 00:00:00NaNNaNPago a MACIAS MERCADO MA. MERCEDES por concepto de servicio de arrendamiento de inmuebles del Enlace Regional Guadalajara, correspondiente al mes de diciembre de 2024, según factura con folio fiscal 01895A1A-7CF1-4A7F-94DD-9109B4C0A363, contrato UR-FPSB-006-2024-906. (Subp pptal 7430). Gasto que corresponde al ejercicio 2024 y se liquida en el año 2025.ENTREGA PATRIMONIALNaNCORTO PLAZO0.0NaNNaNNaNNaN46837.48NaNNaNNaNNaNNaNNaN
3149402442380320FONDO DE PENSIONES DEL SISTEMA BANRURALFONDO DE PENSIONES DEL SISTEMA BANRURALXXXXXXXXXXXXX1020| ||DISTRITO FEDERAL|ALVARO OBREGON |GUADALUPE INN|INSURGENTES SUR |# 1971 TORRE III PISO 6 ||| ||38871NFF03063045840141762025-02-19 18:15:552025-02-20 00:00:00NaN2025-02-21 00:00:002025-02-21 00:00:00NaNNaNPago a ITURBE MANDUJANO MARIO por concepto de servicio de arrendamiento de inmuebles del Enlace Regional Tuxtla, correspondiente al mes de diciembre de 2024, según factura con folio fiscal 05A9D980-73B2-4049-B824-6145A9A25D91, contrato UR-FPSB-013-2024-912. (Subp pptal 7430). Gasto que corresponde al ejercicio 2024 y se liquida en el año 2025.ENTREGA PATRIMONIALNaNCORTO PLAZO0.0NaNNaNNaNNaN22022.00NaNNaNNaNNaNNaNNaN
3149403447880615FIDEICOMISO DE COBRANZAFIDEICOMISO DE COBRANZAXXXXXXXXXXXXX11000| ||DISTRITO FEDERAL|MEXICO |LOMAS DE CHAPULTEPEC |AV. PASEO DE LAS PALMAS |# 405-404||| ||74306PIR090827MJ740141362025-02-19 17:29:552025-02-20 00:00:00NaN2025-02-21 00:00:002025-02-21 00:00:00NaNNaNPago a las facturas 386/44741 y 386/44935 a Regus Management de México, S.A. de C.V. con fecha 05/01/2025 y 06/02/2025 respectivamente, por Pago de renta de oficina virtual por los meses de febrero y marzo 2025 para Promotora de Infraestructura Registral II, S.A. de C.V., SOFOM., E.R., cuenta destino a nombre de México Business Center 39, SA de CV, empresa Filial que opera en gestión de cobranza como agente por y en nombre de Regus Management de México, S.A. de C.V.PAGO DE ACREEDORES DIVERSOSNaNCORTO PLAZO0.0NaNNaNNaNNaN306.24NaNNaNNaNNaNNaNNaN
3149404442380320FONDO DE PENSIONES DEL SISTEMA BANRURALFONDO DE PENSIONES DEL SISTEMA BANRURALXXXXXXXXXXXXX1020| ||DISTRITO FEDERAL|ALVARO OBREGON |GUADALUPE INN|INSURGENTES SUR |# 1971 TORRE III PISO 6 ||| ||38871NFF03063045840141652025-02-19 17:55:442025-02-20 00:00:00NaN2025-02-21 00:00:002025-02-21 00:00:00NaNNaNPago a MAYA TALAVERA MARÍA TERESA por concepto de servicio de arrendamiento de inmuebles del Enlace Regional Zamora localidad Morelia, correspondiente al mes de diciembre de 2024, según factura con folio fiscal 350A166D-6600-474D-81E2-6A3CF16F6DBE, contrato UR-FPSB-008-2024-908. (Subp pptal 7430). Gasto que corresponde al ejercicio 2024 y se liquida en el año 2025.ENTREGA PATRIMONIALNaNCORTO PLAZO0.0NaNNaNNaNNaN10010.00NaNNaNNaNNaNNaNNaN